{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cfear-radarodometry-conservative-filtering-1","title":"CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry","arxiv_id":null,"date":"2021-09-16","proceeding":"IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 9","authors":["Daniel Adolfsson","Martin Magnusson","Anas Alhashimi","Achim J. Lilienthal","Henrik Andreasson"],"abstract":"This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.","url_abs":"https://arxiv.org/abs/2105.01457","url_pdf":"https://arxiv.org/pdf/2105.01457.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cfear-radarodometry-conservative-filtering-1","repo_url":"https://github.com/dan11003/CFEAR_Radarodometry_code_public","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"cfear-radarodometry-conservative-filtering-1","repo_url":"https://github.com/dan11003/CFEAR_Radarodometry","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"radar-odometry","task_name":"Radar odometry"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"huber-loss","method_name":"Huber loss"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/radar-odometry-on-oxford-radar-robotcar","task":"Radar odometry","dataset":"Oxford Radar RobotCar Dataset","model":"CFEAR-3-s4","rank_in_archive_order":1,"of":1,"metrics":{"translation error [%]":"1.31"},"uses_additional_data":false},{"leaderboard":"/sota/translation-on-oxford-radar-robotcar-dataset","task":"Translation","dataset":"Oxford Radar RobotCar Dataset","model":"CFEAR-3-s50","rank_in_archive_order":1,"of":1,"metrics":{"translation error [%]":"1.09"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}